Abstract
Linear quadratic regulator (LQR) is a control strategy that has found a wide range of applications. Even though LQR possesses high performance and good robustness, designing these controllers has been proved to be difficult, primarily due to lacking of a proper selection methodology to select Q and R weighing matrices. In this paper, we propose a deterministic approach to their selection, giving the designer actual control over performance parameters. This method has been by validated by applying it to the stabilization of a practical inverted pendulum system. MATLAB simulation results reveal that the proposed method results in good stability, settling time and overshoot.
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References
Athans M, Falb PL (2013) Optimal control: an introduction to the theory and its applications. Courier Corporation, Chelmsford
Sargent R (2000) Optimal control. J Comput Appl Math 124(1–2):361–371
Comissiong D, Sooknanan J (2018) A review of the use of optimal control in social models. Int J Dyn Control 6(4):1841–1846
James M (2021) Optimal quantum control theory. Ann Rev Control Robot Auton Syst 4:343–367
Maciejowski JM (1989) Multivariable feedback design. Electronic systems engineering series
Safonov M, Athans M (1977) Gain and phase margin for multiloop LQG regulators. IEEE Trans Autom Control 22(2):173–179
Priyambodo TK, Dhewa OA, Susanto T (2020) Model of linear quadratic regulator (LQR) control system in waypoint flight mission of flying wing UAV. J Telecommun Electron Comput Eng (JTEC) 12(4):43–49
Priess MC, Conway R, Choi J, Popovich JM, Radcliffe C (2014) Solutions to the inverse LQR problem with application to biological systems analysis. IEEE Trans Control Syst Technol 23(2):770–777
CRAMER E, LEE T (1992) Test flight of LQR missile guidance. In: Astrodynamics conference, p 4532
Starin SR, Yedavalli R, Sparks AG (2001) Design of a LQR controller of reduced inputs for multiple spacecraft formation flying. In: Proceedings of the 2001 American control conference.(Cat. No. 01CH37148), vol 2. IEEE, pp 1327–1332
Priyambodo TK, Dhewa OA, Susanto T (2020) Model of linear quadratic regulator control system in waypoint flight mission of flying wing UAV. J Telecommun Electron Comput Eng (JTEC) 12(4):43–49
Elkhatem AS, Engin SN (2022) Robust LQR and LQR-PI control strategies based on adaptive weighting matrix selection for a UAV position and attitude tracking control. Alex Eng J 61(8):6275–6292
Yu W, Li J, Yuan J, Ji X (2021) LQR controller design of active suspension based on genetic algorithm. In: 2021 IEEE 5th information technology, networking, electronic and automation control conference (ITNEC), vol 5. IEEE, pp 1056–1060
Haiying W, Hongwen L, Jinlin J, Rui X (2010) LQR control of circular-rail double inverted pendulum based on genetic algorithm. J North Univ China 2:132–135
Shen P (2014) Application of genetic algorithm optimization LQR weighting matrices control inverted pendulum. In: Applied mechanics and materials, vol 543–547, pp 1274–1277. https://doi.org/10.4028/www.scientific.net/amm.543-547.1274.
Kukreti S, Kumar M, Cohen K (2016) Genetically tuned LQR based path following for UAVs under wind disturbance. In: 2016 International conference on unmanned aircraft systems (ICUAS). IEEE, pp 267–274
Bhushan R, Chatterjee K, Shankar R (2016) Comparison between GA-based LQR and conventional LQR control method of DFIG wind energy system. In: 2016 3rd international conference on recent advances in information technology (RAIT). IEEE, pp 214–219
Tan W (2012) An improved method for rectilinear double inverted pendulum LQR controller parameter optimization. J Chongqing Univ Technol (Natl Sci) 85–88
Wang H, Zhou H, Wang D, Wen S (2013) Optimization of LQR controller for inverted pendulum system with artificial bee colony algorithm. In: Proceedings of the 2013 international conference on advanced mechatronic systems. IEEE, pp 158–162
Peng Y, Chen Y (2014) Optimal design of the LQR controller based on the improved shuffled frog-leaping algorithm. CAAI Trans Intell Syst 9(4):480–484
Almobaied M, Eksin I, Guzelkaya M (2016) Design of LQR controller with big bang-big crunch optimization algorithm based on time domain criteria. In: 2016 24th Mediterranean conference on control and automation (MED). IEEE, pp 1192–1197
Deng X, Sun X, Liu R, Wei W (2017) Optimal analysis of the weighted matrices in LQR based on the differential evolution algorithm. In: 2017 29th Chinese control and decision conference (CCDC). IEEE, pp 832–836
Nekoui MA, Bozorgi HHJ (2011) Weighting matrix selection method for LQR design based on a multi-objective evolutionary algorithm. In: Advanced materials research, 383–390. Trans Tech Publication, pp 1047–1054. https://doi.org/10.4028/www.scientific.net/amr.383-390.1047
Oral O, Cetin L, Uyar E (2010) A novel method on selection of Q and R matrices in the theory of optimal control. Int J Syst Control 1(2):84–92
Kumar EV, Jerome J, Srikanth K (2014) Algebraic approach for selecting the weighting matrices of linear quadratic regulator. In: 2014 international conference on green computing communication and electrical engineering (ICGCCEE). IEEE, pp 1–6
Kafetzis I, Moysis L (2017) Inverted pendulum: a system with innumerable applications. In: School of mathematical sciences. http://ikee.lib.auth.gr/record/288541?ln=en
Hehn M, D’Andrea R (2011) A flying inverted pendulum. In: 2011 IEEE international conference on robotics and automation. IEEE, pp 763–770
Anh N, Matsuhisa H, Viet L, Yasuda M (2007) Vibration control of an inverted pendulum type structure by passive mass-spring-pendulum dynamic vibration absorber. J Sound Vib 307(1–2):187–201
Ogata K et al (2010) Modern control engineering, vol 5. Prentice hall Upper Saddle River, New Jersey
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning (1st. ed.). Addison-Wesley Longman Publishing Co., Inc., USA. https://dl.acm.org/doi/10.5555/534133
Conn A, Gould N, Toint P (1997) A globally convergent Lagrangian barrier algorithm for optimization with general inequality constraints and simple bounds. Math Comput 66(217):261–288
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Chacko, S.J., Abraham, R.J. On LQR controller design for an inverted pendulum stabilization. Int. J. Dynam. Control 11, 1584–1592 (2023). https://doi.org/10.1007/s40435-022-01079-0
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DOI: https://doi.org/10.1007/s40435-022-01079-0